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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Linear Regression\n",
      "\n",
      "*Adapted from Chapter 3 of [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/)*"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Part 1: Introduction"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- **Classification problem:** supervised learning problem with a categorical response\n",
      "- **Regression problem**: supervised learning problem with a continuous response\n",
      "- **Linear regression:** machine learning model that can be used for regression problems\n",
      "\n",
      "Why are we learning linear regression?\n",
      "\n",
      "- widely used\n",
      "- runs fast\n",
      "- easy to use (no tuning is required)\n",
      "- highly interpretable\n",
      "- basis for many other methods\n",
      "\n",
      "Lesson goals:\n",
      "\n",
      "- Conceptual understanding of linear regression and how it \"works\"\n",
      "- Familiarity with key terminology\n",
      "- Ability to apply linear regression to a machine learning problem using scikit-learn\n",
      "- Ability to interpret model coefficients\n",
      "- Familiarity with different approaches for feature selection\n",
      "- Understanding of three different evaluation metrics for regression\n",
      "- Understanding of linear regression's strengths and weaknesses"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Libraries\n",
      "\n",
      "- [Statsmodels](http://statsmodels.sourceforge.net/): \"statistics in Python\"\n",
      "    - robust functionality for linear modeling\n",
      "    - useful for teaching purposes\n",
      "    - will not be used in the course outside of this lesson\n",
      "- [scikit-learn](http://scikit-learn.org/stable/): \"machine learning in Python\"\n",
      "    - significantly more functionality for general purpose machine learning"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "from sklearn.linear_model import LinearRegression\n",
      "from sklearn.cross_validation import train_test_split\n",
      "from sklearn import metrics\n",
      "import statsmodels.formula.api as smf\n",
      "\n",
      "# visualization\n",
      "import seaborn as sns\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Reading the advertising data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# read data into a DataFrame\n",
      "data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv', index_col=0)\n",
      "data.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>TV</th>\n",
        "      <th>Radio</th>\n",
        "      <th>Newspaper</th>\n",
        "      <th>Sales</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>230.1</td>\n",
        "      <td>37.8</td>\n",
        "      <td>69.2</td>\n",
        "      <td>22.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>44.5</td>\n",
        "      <td>39.3</td>\n",
        "      <td>45.1</td>\n",
        "      <td>10.4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>17.2</td>\n",
        "      <td>45.9</td>\n",
        "      <td>69.3</td>\n",
        "      <td>9.3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>151.5</td>\n",
        "      <td>41.3</td>\n",
        "      <td>58.5</td>\n",
        "      <td>18.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>180.8</td>\n",
        "      <td>10.8</td>\n",
        "      <td>58.4</td>\n",
        "      <td>12.9</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "      TV  Radio  Newspaper  Sales\n",
        "1  230.1   37.8       69.2   22.1\n",
        "2   44.5   39.3       45.1   10.4\n",
        "3   17.2   45.9       69.3    9.3\n",
        "4  151.5   41.3       58.5   18.5\n",
        "5  180.8   10.8       58.4   12.9"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "What are the observations?\n",
      "\n",
      "- Each observation represents **one market** (200 markets in the dataset)\n",
      "\n",
      "What are the features?\n",
      "\n",
      "- **TV:** advertising dollars spent on TV for a single product (in thousands of dollars)\n",
      "- **Radio:** advertising dollars spent on Radio\n",
      "- **Newspaper:** advertising dollars spent on Newspaper\n",
      "\n",
      "What is the response?\n",
      "\n",
      "- **Sales:** sales of a single product in a given market (in thousands of widgets)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Questions about the data\n",
      "\n",
      "You are asked by the company: On the basis of this data, how should we spend our advertising money in the future?\n",
      "\n",
      "You come up with more specific questions:\n",
      "\n",
      "1. Is there a relationship between ads and sales?\n",
      "2. How strong is that relationship?\n",
      "3. Which ad types contribute to sales?\n",
      "4. What is the effect of each ad type of sales?\n",
      "5. Given ad spending in a particular market, can sales be predicted?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Visualizing the data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Use a **scatter plot** to visualize the relationship between the features and the response."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# scatter plot in Seaborn\n",
      "sns.pairplot(data, x_vars=['TV','Radio','Newspaper'], y_vars='Sales', size=6, aspect=0.7)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "<seaborn.axisgrid.PairGrid at 0x18c0dc50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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ApLV6La29Zu8/NqYNa/q1a++IpNbrvSn5AflFzQNCFnYDzoQLRBST74E8MDF2JstnteeV\nN1XoaNMd11stHcukDiugkbDq52T5rH5w4IS2blqrrs6Olo5rYn5A/jBMFLkXxby9ZpOp0zQElHlB\nQDCmxU5Uecfv4hHMlUbaOGPnUzddpp5iV8tPBJPKD8Qgapl59wnEJMleOXrUAcQt6bzDkxCkVdKx\nExZiEE48GURumdBrn4blmNP0FBMwiamxk1TeMSHnAq0IM3aSyA/EINwkf1UCYLys9IgCcSN2ANRD\nfoAJeDKI3DK1195UaXiKCZiI2JlGzgXmizM/EINwQw1AruW1V46lrIH4EXf5zbmAKaox2Capoum8\n1Jt0oZAoMjFyr3pDYtKNWpRlCTp53KTzA6RBbcxEtWiDaXHppTymlBXhGS+dkST1FLvm/c60Oor5\nOenejQNaeUHwPUeRbkQmILNW14qyLF43u4+zTEAW1cbMp39/IFDc+XkPE+LStPIgHs+//At97XtH\nJUmfvPESXXP5e2Z/R50wk/Ne4MHH94WSk5BOzBlE7pm0upZJZTG5TIDJnDHzwv4Tkb9H0nFpWnkQ\nj9dPvKWvfe/o7Pf+6DNHZ58SUieAdKAxCOQIk8eB+B18bVx33bKCuANgBOe9wObbB8hJOUZjELln\nUgMpjrJUJ49v27zO05Adk84PkAbOmPnUTZfpSqvXV9z5fY+k49K08iAe7z9vkT554yWz3/sdN1wy\nO2+QOmG22nuBDWv6ky4OEtRWqVSSLkMzlbGxUqIF6O0tijKYUYYoy+FnknvU58JLWeL+PtzKZFCd\naIvw8InnIDemnHsnyvWOOOI4qsU5gpYr6sVCDK5fUeYgyfA8ZNoCMgbXE8rlA+XyJ2geoosGmGFS\nj6VJZakysUyAyeKIGdPi0rTyIB5ujcAq6gRgNoaJAilTmih7moTv9XUA4uEWk2MnTxOngA/Nrm2l\nibLGTp6OsURAukXWXWNZVqekv5J0gaQuSf9N0iuSviLprKRDku6xbdv4capAGMIYKuN1XyCW8wbM\n4haTfuOU/dqQd81ixqRrH/GKtIjyyeAdksZs275a0o2SHpK0TdJnZn7WJumWCN8fMMbQ8Li2bN+t\nLdt3a2h4PNAxnMt0P/j4PtfeUZbzBsziFpPjpTO+4jSMHAKkWbNrm0nXPuIVaRJlY/Abkh6oeZ9J\nSYO2bb8w87O/l3RdhO8PGMGkCxSA9CGHAOlBvCJtImsM2rb9r7Ztn7Isq6jphuF/cbzfKUnviur9\ngazxui8Qy3kDZnGLyZ5iF3EK+NDs2sa1Dwgm0q0lLMvql/RtSQ/Ztv0Vy7JGbNvun/ndLZKus217\nc5PDMKcQqffsnhE9+Pg+SdLm2wda2tOnOjG+d/EC3z/LsEi3lojw2MiQZjHXSpyGmUMQici3loj4\n+JEJ+1oUJM7iRrwiIYHyUGSNQcuylkh6TtKf2ra9a+Zn35G0zbbt5y3LeljSP9q2/Y0mh0p8bx0T\n9hOhDGaVI0gZ6k0mDzrJvFqGJCfMm/BdzJSDfQYNkddyBY3DZuWqzQ9xLkiR1+8xqLzvM1hPUtcn\nE+qJyXvzOsVVLr85LO/ny6+geSjKOYOf0fQw0Acsy9plWdYuTQ8V/axlWS9qeiXTb0b4/sg5k7ZW\nqJcAW51kztwEIHlRxaEzPxS7C7ONwiTjPOn3h/lKE2WNl87okacP5/b6VI1Xv7IaXyyqY67Iuhdt\n2/4zSX/m8qtro3pPoMqk5aXrlaX2BlKSHn7yoLZtXsccBwCu+eHzm9fp2MhbieY2k3IrzFRbRzas\n6ddzQ8c1WT6bcKnSIavxxf2O2dh0Hplj0tOyqMvChHkgeXHF4cTkVKK5zaTcCjM568iuvSMaWNbL\n9ckD4gtJISqBhFRvIB9+croXMOiFcnBpj7ZtXjd7TADxCzsO3fJDV2dHy8cF4vaJ9Rfrjustrk+G\niXMOclj3O4gG3wQyx6Sk89rxX2nDmn7t2jviWpawbiBJqkDywo5Dt/zwyRsv0aPPHJUk3XHDJbHG\nvkm5FWZyqyM9xa6ES5UOccaXczjqDb3FSN6nFh3X5uLbgFHC6qkKM+kELVNpoqyHvv2y2tvbtGpZ\nn9rbpGX9i+a9jqQIoJ7a/FCaKOux7x/TqmV9kqTH//GYBpf1znuN8+/CFFZujfOpBOLFTX9wcZy7\n0kRZjzx9WAPWdB758ncPa/DSJZG8l1OcuQre8Q3AGGFPnA4jwYRRpsnyWe155U0VOtp0x/VWy2UC\nkF/VfCJJhY65q4jHtfhEq7k1q4tk4B3c4AcX9blrk7Ru1Xl6ds/0iKUk9kAkB5iFBWRgBBMnTrda\nJhZ3ARCmRjnFxBzqJi3lBLKqIunZPSNzFvmJEznAPNyZIheiGo7Q7LgMlwEQhmquiTqnVN+nN/Qj\nA/nSyn0HQygRJ54MwghRPkULutFpszJ5PW6QjWcbbTqb1Q1pgSwoTZR1aiZGw4rTepvP1wojh9a+\nT3UIWdgYMYFGorq+xX3dbGWD9ag3Z3eLwd7FC0J/Hz/vTw5IVlulUkm6DM1UxsZKiRagt7coyhBP\nGbz0hvkpR2mirC3bd89udFroaPO90alrmQodumvr91s6bj2NxtLX/u7ejQNaecH8BWniZEK9nClH\nW/NXBZZ4DnJjyrl3ynO5hobH9cjTh+fMx2k2H6ZZufzmsFYWvGo1V/p9P8l/OQ2uX1HmICkHeSjM\neWS15Yp7flqjWAo73lstpyRP5YpC2Pd7cTK4XIHyEE8GYZQgT9GiFmeZGo2ld/7uwcf38YQQMEQ1\nPldc1DNnPk7c82FMzKFu0lJOxCOqeWTMT6sv6RhM+v3xDhqDyLSohiP0Ll7AMAcAkYtrSJXzfTbf\nPkBOAwJoJWYZQokkMEzUAxMeB1OG1soR9mTsahmimOQ9NDw+Z9NZ5zDR6u82384w0ZpyMEzUEHku\n19DwuL783cP68BXnza7Q54zhoOWKa0GJ6vu8v39xbr/HIBgm2rpG175WyhXmcf1wi1nT4r3K4Lii\nXD4EzUN0NyB1xk6eVmmi7CtJRpVQozhuo9UCa39n6s0akFeDS3u09O4PqU3STWsvlBRejojrpjDs\nuYiAV1GtlJvUqt6tvFdcnT7EMyQag0gZ0zcqDSvBNvp7kjdgLi/xmbYbMdPzLtKtNh7S1HGbVsQz\nnJgziNQwfSJ41MtBA0i/tOUJ0/Mu0i1t8ZB2xDPc0BgEfKi3VxEJFkAzbnli7OTp2d+RM5Anebhu\nEtdIAxqDSI2kV9mKY0NmAPlj8tORpPMukFYmxjXxDDc0BpEqg0t79KX7r9O2zetiHefebI8/EiyA\nZtzyhCTjn45UF+CIO+8i27J83TT5qSfxDKdsRB1SKegiCr2LF0jlqdCP26p6K5albbEIAOFwi/0k\nVjYMIwexyijCUltnklrps5m01muv5U7b50K0qA1IRFSrWUV13GoPZu0ef27J1PkzVu0C8qlR7M/Z\nd2zxgjm5JeynI1HmIPIb/HKrM6Y1TMKo1857hjieehKPCIphoohdVMMnoh6WUTu0YsOa/sTLA8BM\nfmM/qmFbUeYg8hv8SkOdCbOMcQ7HTMO5hbnM6o4BDGdaDyaAbCC3ANlDXCMNeDKI2EU1ady0yeim\nlQdAPEyJ/SjLYcpnRHqkoc6koYxu0lpumKGtUqkkXYZmKmNjpUQL0NtbFGUIvwyBF5BpUo44Jn77\nORdRladahiQnuptQL2fK0Rbh4RPPQW5MOfdOaSlXHHHj5T3iOF9BPqvXcsWdfwyuX1HmIClDeci0\na7Sb0kRZbZKqd8hhlTXq+hvVfVVSKJc/QfMQ3QZITFQXAtN6w6IsDxPGAf/iihtTclGU5TDlMyI9\n0lBnit2FVF5f03BuYR6GiQIpNXbyNBPGAZ9YaAFAM+QJ5AldCEBETNmnyJRyAEkpTZSlk6eD/62I\nH8CpUVwRN0B6EKVIjerFpTfm9wtyMYtjeImX/cnSOMwFCJNbDHjd/yvM+Inz5pgbcUStUWy4/S7K\nOhnFsZPYJxBICjUbRmiWzGsvLvduHNDKCxZFWp5WbgJrh5dI0sNPHtS2zesiuZBU9zGS5p+7OMsB\nmKheDDSKm2Z/a2rnUFzvRUMTjWLD+bsvf/ewyh9drp1PHZIUfp2Msr57yRNRIc4QJ+YMInFDw+Pa\nsn23tmzfraHh8Xm/d47df/DxfZGO3U/bXIFid4ELBuBTXHETZz6J+r2a5WrAaeVFPdr51KFI6mQc\nsZXE9ZU4Q9xoDCJRSTW8ShPlyN6n2F3QPbddriuXL9GVy5foT2+9PHN7jAFp0EoMFLsL+vTvD+jK\n5Ut0TleHMfETZe5q9r5p6iRDdBrFlfN3V686L+HSJstvvEYdZ0nlD5gt+Ssb0IRz7P7m2wdauilr\nNqwkjLkCb0+d1d6jo5KkKy9dErisrUpymAtggmoMFBd2SeUpz39XmyfuumVF4HlPYc49iiN3AV40\niivndaeVOtko5kyv76bN2TetPDAHm857YMLmklkuw9Dw+JxkXi9BVS8K7+9fHHjT+dJEWVu2756d\nz1DoaKs7D6jRcRqdiyM/f0tf+Po+T+/RiizXiQDlYNN5Q2ShXG554r6NA/rC1/dJCnYjVS+fuJXL\n7bVh5S6v3MrlNVdHyeD6xabzDQSpk14bL27HTrqe1IvXZvcvUvA4a+XeJ+nzVQ/l8odN55FaXp9e\nebmIhNnzFeRGqjRR1vP7TwR+TwBmen7/iZYWk/H62jByWFRPRxhpgKD81hc/CzhlrS4GiTOe+qEV\nzBmEEcKYpO1lrH0c8+gOvjqu9Wv6Z9/jzo+tyNzFCsgyZ56482MrdPDV6BdyaJTDTJkDzIJVQHOt\nxqufODPl3gfpRU1Ay9K2BHKUvdvF7oLuvPkyffm7hzVo9enqVedp+fnRboMBIHzOPFFovyzxuUlZ\nfzKXtmsJomP6fEAv4orXM5Pe5kJnPX8gOGoDWmLS0AQ/F4+gQ0Br/7bejcvg0h4tvftDgd8HQPya\nxXccN1JeclhWc4pJ1xKYoRpzbZIqmo7JJOt/0AWkojQ0PK5Hnj6sDWv6tWvviKTw732QfdQKBGbi\npuZR3bA5b1S6fv6W/qLBYhIkXCA9auP7ntsu19lKxbVhEkdc57H33sRrCcxQ7C4Y0VFgQhmcauPm\nuaHjGrT69In1F6un2JV00ZAyzBlE5oQ9p8VtPP4LM4tJsN8WkG7O+H7pyBuJ76fHvDxgmgn7W5pQ\nhmYmy2c1ZI+qq7Mj6aIghWgMIjAmJAMAWsW1BPCPuEFYqDVoSR6GNLnN4+nqKmifPTr776x+diDr\nnPF91fJzdeWlS1K9cEUa5eFaAv9MWEjGhDLUQ9wgDNQctCwPCciZcHt7izq/b+HsvwGkl9sNFTdY\n8eNcw40JDR4TylCPaeVB+lCDAI+yvqofy7ojSUnXv6zHN5BmJsSjn33//LweSBo1FaEg+aWbiSul\nIT+of+YitwPeZSmXEfv5wQIyaNnQ8Li2bN+tLdt3a2h4POniwKc0rJSG7KL+mYvcDniXpVxG7OcL\njUG0xG/yK02UU5scm4nisyVxvjoL7TozOZXZ7wnm6Sy0a82lS7Tm0iXqLDS/LLUSF1nOQfUE+cxj\nJ09n5sYW8QtS56KMzTzGfVBJN2r5ruLHs1/EJkvDJ5yi+Gxxna/aldI6C+26/SPLdP+OFyN/X0Ca\nrn8br1umr33vqCTpkzde0nBYUitxkeUcVE8ePzOSFaTORVlPk7iWSmatOpoWzu/qht5iwiXKB99P\nBi3L+o0oCoJ08rrPTdI9TVGK4rOdmijrx6+8qQGrT+3tbZGfr+pKaZ+98yp97XtHM/k9wUylifKc\nOvfoM0fn1LnaXuJWYi2PT7paOV+9ixewhxl8C1Lnorw/8HPsMJ5IVa+l2zavS23HS1L7F7p9V2Mn\nT0f+vvDwZNCyrJslrZP0f0j6iaQ+y7L+q23bX4y6cEgHk5dcDsKESdNHfnZSQ0en9zFcv6ZfPzxw\nIvL3zMJ3h2xx9hIv61/U8jE7C+26Ymmv2tuktpaPlm1Zy+0w05nJKQ1e0qf9x8Y0WT6bSBnCfHqY\nhVgh9vPFy5PB/yrpryRt1HRj8AJJfxJloZA+xe5Cw4SRVE+TX0EmTYf92UoTZe186tBs79hze0d0\nz8eviOVVpsBoAAAgAElEQVR8peV7QnbUq3NuvcQVKXD97F28QPfcdrmuHXyv9tmj2nt0VMdG3or2\nwyUsjHhultuBWn7r3NDwuO7f8aKGjo7q2sH36pyujlCvO17Kk+WRS62IO/bdvqvexQtie/888/Qt\n27Z91LKsP5f0qG3bpyzL6oy4XMggPz1NSTydGy+dmb0gSNLDTx7Uts3rPJUhaC+a18/ZP7PBfRzo\nEUTc/NS5waU92rpprSSpp9jl630ueu+79NC3Xw4U416YMKrAiXhG3LzWudpGmCTt2juirZvW+o7r\nZpb2L9LWTWvV3dmhhS7lOTM5Fer7hcXEfBI18lUyvJzpNy3L+qKkD0j6A8uytkn6ebTFQlZ5Ce4k\nFjwYGh7Xj195s6Vj+E1c9T6nCZPQScKIm9um725xYOqCKKaWSyKeEb+gda6rsyPUcjSLy6HhcT3y\n9GFtWNOvXXtHJJmx8IvJ+SRqSZ/7PPIyTPTfa3p46LW2bZ+SNDzzM2RYUkv7JjFco/qe+4+Naf2a\n/liGSDb7nFmYhA60yhkHreaHNkkbamJ8/er+UOYNMsxsGkvCw4+opyU0i8vq7yfentJzQ8c1aPVp\n66a1iV9zveYT4g1haRp1tm3/i2VZU5L+ZGao6L/Ztl2KvmhIShQ9UmkY7jBZPqvnZy4In1h/sXqK\nXYmW2+RzBcQlzGHXFUm795/QqmV9kqQfHDihm9Ze2GoRE2dCfs3zkwwEV29YYNx1erJ8VkP2qO64\n3orl/VqVVLyZkGsQvqZPBi3L+u+SflfSbZI6Jf2xZVmfj7pgSEYUPdx+FmVJYgGT2vesVCq68tIl\n6il2BVpMJsh7slAL4E2juPESr8Xugu68+TLtPzaq/cdG9ambLgsl7pKM5yjzlFc8GUUrnAuVhFWn\nm8WlqdfhZuVKKt5MyDWIhpdaf4OkQUl7bds+aVnW70g6KGmLlzewLOu3JX3Otu31lmUNSHpa00NN\nJWmHbduPByg3UsI5QdzLgg1JTCB2vmeQcrf6ngCac4sbP/EaVdwlEc9x5CkgTmHX6WZxaep12LRy\nkWuyzcucQecyS10uP3NlWdZ/krRz5m8kabWkz9u2vX7mPxqChjGlp6z2Ji/O90xioRbTkinzEBCG\nKOtRq3ETVdx5PW7WYsyU6wbMllS997L1lYn1tV65iDeEzUvt+Yakr0v6TcuyPi3pDyT9jcfjv6rp\n4aVfnfn3aknLLMu6RdNPB++bWZQGBgmzRyroyphRjYf3Mt69+pp7brtcf/nEy5LMWF0sDsz7QRji\nrkfF7oI+/fsDemH/CR18bTzw8M845sM4z80NvcXAxzJh5eEq055kwCxec4KzTq9f3a/Xjv9Kqy5+\ndyTl8hPzJs2XizveTMo1CF9bpVJp+iLLsm6UdJ2mnyQ+a9v2d72+gWVZF0r6G9u2P2hZ1h9LOmDb\n9j7Lsj4jabFt2/+xySEqY2PJrlfT21sUZWitDH4T7pbtu2eHIxQ62uYMRwhaDi8XI+drlvUvUsWl\n3GF8H61eWMKsE6WJss5MTun+HS/WPe9Rl6EVvb3FMBaFrCfxHOTGlHPvVHp7Slv+4gVf9ajpMZvE\nSm3c3nXLCl1p9c57TbPzFUcD1i23fen+66Rya/ucRXGTamr9MrhcUeYgKcV5qNk13enURFlf/Qdb\nlYp0YHhMlUrFdw6plqtRbPiJ+bDyQ1j1N+yY91quuBvEBse7qeUKlIfqfpuWZV2j6cXXJOnfND3X\nr/q7q23bfiHA+z1h2/avZv7/SUnbvfxRbws9p2GhDK2VYf6tWQMnT8/7UXFhl3oXLwhcjrGTp+eN\nd//S/dfNOaaX19Rq5ft4ds+Itj+2T5J078YBbVjTH+g4YdSJalkGL+mb9zvneY+qDKYz9TOaVq5n\n94zoBwdOzPu5l3rU6JiNYsUZt49855CurBO39c6X39gPzCW3NSqXV77yq5/jGla/qkwtV9RM/dxN\ny+Xhmu58/dDR0TmNxyA55ODP3qqbO/zEfNj5odXvMaz7Bycv5Yoq1zR8z7TW+xRp1LT/rN5pDLpZ\nH+D9vmdZ1r22bf9U0kck7fHyR0m3vk3oAWi1DGH05sR5HpzDEVSemn3vIOVw3aPn1Jk5PfJeXlPV\n6lPS7Y/tm72wPPj4Pl24ZKHv7yasp5PVsuw/NjZv493a8x5VGcIQdVI24TM6mXLuq6p1qb29TevX\n9Os5H/Wo2THrxUr1ifa8v3OJ20bny0/st8qZ23oXLzDqe6wyqX7VXr9MKletOG4MTf3cXsrV6Jre\n6utd728KHU1zx7zj1In5MPNDq0/gwrp/CFquuFEuf4Lmobq1x7bta4MWxkW1UblJ0kOWZU1K+oWk\n/ynE90Ad1eENnYV2/S+fuEL9fa0njqi5re5Z/f8gvIx3L3YX9MkbL9GjzxyVJN1xwyXGn6cwTZbP\n6gcHTmjrprXq6uzI1WdHeNz264xCbV6rjdsgc1ka5Yewh0Uxt+4dXs5tmHMskRy/9d7r651DxJdf\nsFgLPRzfzxy4OPODxNx9xK/pnEHLstZJ+o+Sfl3TcwY7JJ1v2/aFkZduWuLj5E3oAQhahupY/fb2\nNl0z+F7t2jPdWx8kwSR1HubdDKx9X8NyNErOzX73n//yh1px0fR5Ofz6uD5394dcX9vquRgaHp9z\nYQmS7MP6PlopiwmxMVMO5gwaIIx67eWYzjlI53R16LN3XtWwE6P2fDXqda/9eRw3ZSZ+j1L05fJy\nbqOaYxkF5gzGz61+DFh9+u1Ll+iGte/TMy/+U9N81MoCMkHyQ7Pz5SW3RZFn85qHgjK4XOHOGazx\niKT/LumPND3H76OSvhXkzZCcK5b2ateekdTtEeO2t83gpUvqvr5RcvaS9CfLZ7XnlTclTV9YomLS\n0wGTyoJ0G1zaoy/df51Kp84Y+yStUY6ot7GzlJ6cmQacW0SlUnnnPsFL7gi6imgcdbiz0K4PX3Ge\n7t/xoqR38hXXbITNyz6D/2bb9l9Jel7SSUl3SfpEpKVCaKrDG9qj7rM0QG1ynjpb0cNPHpxN4kPD\n49qyfbe2bN+toeFx17+Pe+8ek/Y2MqksSLfexQtCr0vO+lkbq+d0dej2jyzT/TtebBjfUuMcAfO4\n5eTQF/VBajnrx7Wr+3VgeGzea1rJR17uHcJU+5kGlvXq2ZlOfGe+4pqNMHlqDFqW9ZuSbElXaXr+\nXxILCiGgwaU9+uT1lu66ZUXqNikN42bAzw1gtcdt2+Z1jNMHDFaN1c/eeZW+9r2joTfw2Ng5On7O\nLTkZjVTrx30bB/TDAydUqVRC6zRodO8QZX6ofqZPrL84lOMBzXipuZ+X9LikWyX9VNIdkoaiLBTC\nt7C7oCutXl0a89CCMCZXex0SUW+St98bQ274gHQIc6EYN2EOxzJpw2oT+Dm3nDM0ip9id0HLz1+k\nz939obqviUKUwzWrT/7Y6B1xaFirLMu6WdMNv+sl3SLphKQJSX8ceckQiTgTSZiLL3gtt1tydibU\nOz+2QjkYNQtkQrNGVNQNvKjnKeYZN7bZElWHh9f4iaJB5mUV8igxPxBxaLTp/H+Q9PuS/lDSCkmP\nSrpX0mWS/k9J98VRQJjFa7JPcoEAt/eoJtSR0VP64jcPaLJ8lpsywHBebwKTaOB5VS8XVjHnAlkQ\npMPDy/1E0osNmdAYoxGIqDWaM/iHkq6xbfuIpP9R0lO2bT8iaYukG+MoHMzy7J6RWCdSR+ELX9+n\nibenWDwCMJzfxV7StKDCyOip2Vz67Mx2P0BaBVmYKe6FWVqRptwCBNGoMXjWtu1/nfn/9ZKekSTb\ntit6ZxN55ERpoqztj+3zdWPG4gsA8s6ZC+/82Ap98ZsHZnPpg4/vo1MKueKn8ci9BBC9RhFVtixr\nsaY3mx/QTGPQsqzzJU3GUDaknAnDK2oxGRtIjyzFa20ubNP0fqZAVkQdq6bdSwBZ0yiqPidpn6RO\nSY/Ytv0Ly7L+B0l/Lul/j6NwMEexu6B7Nw7owcf3SfKe7E1L3FxUgPTIUrzWlr/2xnnz7QOp/2yA\n39Vh/TYeiREgOnWjy7btb1qW9SNJPbZtH5j58WlJd9q2/VwchUMwUa3otWFNvy5csjCSY8cpzWUH\n8iaL8Vp74/z+/sUaGyslXKLwsZVG/vj5rrPU0VNFnUdaNayxtm2f0PR2EtV//23kJUJLol7CnCQH\nAK3Lci5lKw14kaUYoM4jzRotIIOUCbKiFwAAYeE6hLyhziPtstMtg8zIw1AL52fMw2dGNpQmymrT\nO0tKh11niQUgH2pjnbiPFucXjVArMiToil4mJYk8DLWo/Yz33Ha5zlYqmf/MyIah4XE98vRhrVt1\n3uz+eGHW2TzEfxySzOlZWgUW0amN9U/eeIke+/4xTZbPpjLu467zfuObvIpmGCaaMdVJ2ds2r/MU\n8FFv/FqaKHsaLlGaKGu8dCbzQy2cw0leOvJGw8/s9fwBUarG5yNPH9aKi3r07J6R0OM07KFW1dg5\nlbMYMmEzb7/XIeSLM9YffeaoVlzUMxv346UzTWPWtGtjXHXeb3wzhBVe0F2XQV57i2qThCQ9/ORB\nbdu8LrQeLa+9UdXXDV7SF8r7ZgW9eTBBbT3csKZf//KvZxIuUXPOMu/ef0J3f/wKrbxgUcIli1bU\nOd0PngbCj46aRxPf2PWqho6O1r3umXptjLrOmxTfyBaeDCISXnujal+3/9iYNqzpV6GjTYWOtkwO\nL6oOJ6l+xquWnzvn39XPTG8eTOCsh7v2jqi9vS2SOHXGRtDjupV5xUU9evDxfcQQYIA2aU4OWb+6\nX+3t7/z//mNjda97XBv9CSuvItuoETlm2tyOyfJZ/eDACW3dtFZdnR2ZTVhu+ytlbb8lZNetV1+k\n7s4O3bT2Qknh1tks7j0WJ9NyOuCmImn3/hNatWx6NNAPDpzQZ++8SrdefZEe2PmSJstnky2goYLG\nN3kVzfBkMOdqx7kv618UWg+bl96o6nvdc9vls6/71E2XqafYFVvCCmPeQZBjFLsLcz6j27/pzUPS\n3OphT7HL00qiQWPLGQt+uOWU9av7dfj1cW2+fSDUGDJtzlIV8/VgumJ3QXfefJn2HxvV/mOjs9f9\nnmKX7rz5sobXvSiuja3Gcpy5IGh8O/Nq3PnL1HyJaW2VSqX5q5JVGRsrJVqA3t6isl4GL2Pwg5Sh\n3qpXzvdb1r9IFZfXuQnjXJQmyhoZPaUvfvNAoBXMqmWIeu5Co1XD4qqXJpShmd7eYluEh088B7lp\n5dz7XY2u9vVudb729729RT3z4j/FPqenXk6pboPx/v7FodXVMOPelBhyolz+RJyDpAzkIWfeGS9N\nzz/uKXY1fJ2XYwUtV6ux7PfvTai/bmWOslytnGMTzpcbg8sVKA/xZBCRjsF36+V3ez+vDcEwVFfj\n+sLX9+nDq85Te3tboM8cx9yFVp6ShMGElQkRriDfabUeOuv8l797WD+xx+Ycb+zk6djn9DTKKQtD\njiHmLAH+OfPO/ld/qft3vKj7d7w4Lw95ue6FcW1sNZbTmAviLnMaz1Ee0RhErjgT03N7R3TF0t6k\nixWZVoZmkMSzobYOhP2drryoRzufOjT3eKffDqvoADLALe/86MgbXFtmMIQSSaMxmBONkk3c89Pi\neD8/ybW9LdhCC6bP6+OpHpx1oNVxbM46f/Wq81xfVztnL464iDMWTY17biiRR63U+1ZjOYxcEOZ1\n2su5yOL9HlrHnEEPTBgb3Mq4fK/jtcMag++V33lLXsvR7PMODY/PrsZ158dWaPkFi7WwhTIE/RyN\neDlmo/NQmihry/bds/sRFTraAu1HVHuuNv3e/HNpQmzMlIM5gw716sDwyFvzvtNW5xBWj7d+9fSe\nfnfefJmvecBhiXN+a1hxH0a5GuW8qPJsUgwuF3MGG3BeS9rb2vSXT7w8++8g824b1ft65XKLh1Zj\n2c/fO+8dwrhOS/7n5TnLHHVckYf8C3LOguYhmucZ40wIS/sXed6kNI5EGMb7NStLs88b9jLLYX8O\nkzbUZUnq7HF+p0HqmzOetm5aq2/selXPDR3XZPlsS5sht3JjFmcdNSUeGuU8k3IJ8i3sLZWCbMBe\nLx6CdILVSjoXBDkXcZc56XOUNnHnboaJZojbuPwzk1ORvmdahyImvTBLPWHN6QpzaIap5wqNNaoD\n9RaECVrfujo7NHR0tOX9wdKaT0zEnF+YptmWSlFqNR6iyE0MoYSbJHI3jcGM6+7siCzZmHizkZXk\n2llo15pLl2jQ6gs8z4v9xlCvDlTnloQ1ri2MuDMxn6RBVnIe4Eec9d5vbvIzjzGM6zQ5AK2itmRI\nNSHUjstf2F1I1VC/MObhxP15m5XZz2cqdhd0z22X65Wfn9Sze0YkSVdeuqSliwTyrdken5s/foV+\nfPQNnde7UO879zcC15lq3BUXdknlYCMSOgvtGrhouq4ffp0ng1655Ty36wH5AFniVu+r11vnGuH1\n4qH2+hzG/Uerw+6DStN9HhpLInezgIwHJkxgbWUBmSjL0GyBET+8JlGTvg8vi9X4vTB4nVRu0nlI\nGgvIeOOsW+d0dehP/t1lGj7+1mznQ5KbqD//8i/0te8dlSR98sZLdM3l7wlcjjDLFSUWbvDH4HKx\ngEzCaq+3924c0MoLFs17jXPxqx1PHFRnoV0br1s2m3uaLTxX717Hy7XbpPNVi3L5k7UFZBgmmkH1\nxuFHsfR4WEMR0zhErFmZk/5MLDUPJ+cc4pUX9einR9/Us3tGEo+90kRZX/ve0dlyPPrM0VTXX1Pi\njzm/yBq32HJebx98fJ9r/LnNl15xUc+c3OOWA5l2gbjFmbtpDOZElAszcLMRLtP2LkI2DA2P64Gd\nL2nDmv6m+wSiNcQfEI0kY6vZvQ5z95BWNAZzIK4nVLW9dX57xdOYRJuVOchnqp63Vnohk34iCfOc\nmijrx6+8qRUX9eiHL/+zBq0+bd20VsvPX6QPLj93TgMxqdirxss5XR26cvkS3bdxwHO8mIT4A+YK\nK04bxVabNCePrV/d33CBrNrr8+HXx/XJGy8JJQc6r90m5qgs4fyGw+y7bdQV1bzAoPyMva8njROg\nm5XZz2dKYk8w0+oRwlP73R752UkNHR2VJK1f068fHjihO663JEmrLn63Ln7vu/Q7H+hXV2dHonVh\ncGmPyh9drp1PHdLeo6MN48DEPfRKE+XItvMhVpFGccVpRdLu/Se0almfJOkHB07oprUXSpobO+Ol\nM5KknmLXvOvz4LLe2f9vRfXvTcxRWcL5DQ9PBlPI7zCJYndhTq/XHTdcEuoNhd+x983KGmbZTk2U\nNV46E1qvZKM5CPXU/r7eMcJ8muD1iSRD2bKr9rv9iT2mr/ztkdm69dzeEW3+xCpJ79wkLewuqKfY\n1XSucdS9sKWJsnY+dahpHJj49K16zh/Y+VJoTxmcxyZWkSatxqkz3zTbO/XOmy/T/mOj2n9sVJtu\nu2J2kZgt23frP//lD/Xsvn/W/Tte1P07XtTzL/9i9u+c+68G/axxrRnA0zAzrwFpRhej4ZzLJNcG\ngCQ9/ORB15Umncd47PvHZnvMHv/HYxpc1htbD/OZyalEerP3v/rLOVs0tNJz5OyBuqG3GOgYjzx9\nWCsv7tE1q87T8vPnr3QWlmZPJIPUI6SD87t95DuHNGD16adH3px9zem3p1e9kxrHRb0n/nfdskLL\nL1ishdQXSY4bk7en9Pg/HtPWTWtDedKa9VjliWe+jIyemr32Nfru6z31GVzao62b1kqafrpXq/a6\n9/7+xXp95ORs7Axc1KO/+X/s2Th69Jmjuux9vznvGEHE9YSqNFHWyOgpffGbBzRZPsvTMISGJ4MG\nq+0NrjZogposn9WeV97Unlfe1GT5bEglnNZo7P361f16YOdLsfdmlybK+tGRN0JZJdGtB2rs5Gnf\nx3jk6cP68KrzNHR0VF/4+j79xB6b/X0UcyZZ2AdV6y7/rdm6dfOH369HfD59cz7xf+Q7h/TVf7Aj\nWYzKSxyYPsd4snw28SG3acATz2wrdhd01y0rZuP02tX9euhbB3Rqotzwu2/01GdoeHz26Z5bnYn7\nulevrGHnqOr5+sLX9+nDq85Te3tbrp+GmX4NSBsag4aqt0xykACIKmhqhypUe+Q+d/eHdM3l79HW\nTWs1YPXpuaHjmnh7KtdJq2qV1atfnTqjAatP7e1teuQ7h+ackziXriaRZpfbQgrn9S3QLddcpI9d\nfZHefOt0KB1ClYoiGfrkNQ5MWuo9ynjKaqwyzCsfll+wWANWn1Yt69PzQ8c1WT6ricmput997bzb\nzkK71ly6RINWn9rkv844O6r//e9Yc6bLhPFUsJGott56bu+Irlja2/wPM86ka0Dapf+KkkNBFloJ\nujhLvWEcbsMial/T1dmhoaOjs0My4lbsLuiDy8/VooVd2rV3+qlq0Juo6gWluuHsnR9bEegYl17w\nm/rrv3tF0juLeLi9Li5pXLAHzTkXUvjx4V/ofb/1G3ryudckSR/5QL/+8KOX6qt/P10Xmz19e/jJ\ng7NP/B99ZnqY6LWr+/X80PHQyuyWT6qNw2bzcU0RZTwRq0irhd0F/falS+Zs2N7V2eH62tph6X/4\n0Ut1fPTU7KioKy9domX9/qdWOGPn8ovfLWn+ENOgqk8/H/nOIUnz82l1T8Nmucyv9rbg9zRZkvfP\nH5a2SiWZm3UfKmNjpUQL0NtbVBJlGBoen02gm28f0MoLvCdCZyMuyLyMefPk1r5PY2MllSam5xtV\nG3qFjjbXOSy15d/0e+GNbffzfZyaKGticiq0uTtBx+u7nbP7Ng60NG8wqXppWhlmytFoFfFWJZ6D\n3DQ693Ni77bLteNbL8+L1yov2zdUX1eNgYe+NR0DbnHtt044Y+Ocrg790cyKolKIc3AKHRo/eVrd\nnR1GzXU0JYac4ihXkGuEwecryhwkpTAP1XLegzi/+2X9i/TpmjzwwRXn6idH3pyXt4ZH3nKtM87j\nR1lPat+r2VoA9e6j/Ko9X3d+LPw52wbHFeXyIWgeMueKiHmck6G9Vrza5HPPbZfrbKXie3Kz26IF\ng5cuCVz+ZgvcNHtNUAu7C6EmzC98fV9oCzn09y0MrVxArWrsnZmc0reff831NV47ipy93MvPX6TP\n3f2hhn/TipUX9cyuKCqFs2BKbU7csKZfl56/WKtmnhAgOTzxzA/n91v73bdJmnBsyVJvVJFbnYlz\ni4Ha97rrlhX6yt8e0cTbU/rpkTe1zx6dk6vCuI+qIlYQJeYMGs7vZGjn2PKXjrwR+rwMP3NYmpU/\nLwsIZHXeD8xV7C6oq7ND+4+NaX3NHMI7P7Zizk1UkPgLc5EGZ2xcveq8UI5b5cyJu/aO6KUjbzA/\nzRAsdJVfxe6Chkfe0qddtmS5avm5DbeRcGtwRT331Plej3znkFZeHN9cNWIFUaFWwZVzntym31up\n3sULZp9OhtFLlbYl093OiZ+y0rOHuFX33vrydw9r0OrT1TXDmEyKP2dstBJnANLBy5Yspl8zr1l1\nnvbZo5Lc5ws2uo8CTGFmdCEwZ/K5avm5utIxeTusRWfqHcfLsM/aFcPSpHpOigu7pLL/8pt6QUN2\nDS7t0VKPwzqrMdlKbAdVe8wwO06cOXH96ulhosQiYBa3LVm8rpbu5R6nlfxVmiirTZr3XsvPX9Qw\nV9EJjDSgZhog7BssZ/IpTZS1ddPaQAsn+C2Tl7H7tSuG1a5QmJYnAMXuAr17SJVmq4VK0h03XKIH\ndr5Ud3GkOOfl1CtzLT95c3Bpj750/3VGLiBjqrGTp0NfARGoVa8hV43tNk2vjtysDnppcLWSv5zr\nMDjfy0uDFTAZNTRhP7HH9JW/PVJ3JaqgkphY7WXYmZdhIQDiUbvQzAM7X9LE29NPBp2xa9KQUilY\nXutdvCDQ0/ysc2tUO29+L3rvu+a9BvCjXueNsyHnXOxp9/4TuvPmy5rGeLORSEHzl/Nv//KJl42e\nzhK1KEeHIDksIJOgIz9/S1/52yP68KrzNHR0VF/4+j79xB4L7fhp2NTXbVgIgPhUF5oJYyP6OKQh\nr6WF2wJCtee3vb1Nr/z8ZC4W+UJ0mi1UVV0YxW2xpxUX9RDjhsjLgn95RGMwIaWJsp7ff0IrL+7R\nrj0jc1anMjXpVYcN1eNlxUxW1QTM0ywuvcRtdWNlpIOXRvUVS3v1bM31iZtyeFXNByZ03rRy38E9\nyzQTvkdEJ3812iAHXx3XJz6yVENHRyM5fpCJ1fXG6HsdluVl7H7aJ1SXJsrSydNJFwNoyu+8uqAL\nIczJD7et1KDLcuthDS9itdFo1Z7f9qi3UUcm1eaDT//+gOe/c1vs6QcHTgSO8dqcUy9/eclLJuyZ\nDESprVJx39jTIJWkF+ro7S1GsljI0PC4/vp7r+gjHzhfT+9+XdL0jY1bQytIGaqrX1W/YS8Tq93G\n6JcmytqyfffsmPlCR1uiY+Zrz0XcyTfuRTQaiapepq0MM+WI8rY18Rzkptm53//qL/WjI29Ikj64\n/NzINll3yw/3bRyYM/85jLhxxrrf2DelrjolWa6h4fE5jeo530uhQ6dOndGxkbfqvyYBBn+PUTed\nU5GHnPngnK4O/dFHl+uR7xySNLcO1YthvwvIuDn4s7e0/bF9kpovZtfoNV55PlahQ6VTZ4xrMHqJ\nq4b5IsFyJcHgcgXKQ2bVxpwZXNqjpf0fVJuka2c2Wg56o+PkNTE5J0fv2juiVcv66k6w7iy0a2BZ\nr85MTiWezOJumJm2iAZQz6mJsl75+cnZUQeLFnbp4ve+q+kqmmF1rryw/4T6+xbOmwckBYsbt1gn\n7lrntvJ09f+rC+6kfSQHkjVZPqvlFyyeV4caXb9brWelibK2P7bP82J29V7j9b3OTE7pkacPNz2W\nn/syybx4IxdkF3MGE1bsLmjhzOTp2iTZyiTdsMd2V4dunNPVoWsH36u9R0d1/44XE51AzPh1oL6J\nyVJWwTUAACAASURBVKk5c7127R3RRJN9PYPmnWJ3QXfdsmJ2Ts21q/t18LXwcgOxHq3qtafR9197\nfQIacZtj57zHyUpMV2Pm/h0vat2q89RZqH9L7fUzm75IC7kgm2gMGibuJOlM3OtX9+vw6+PzxugP\nLu3R5+75cCyLCYyXzmi8dCb047aKieRIi8mpsxq8pG/OzUlXZ0fd17ead660enXfxgENWn364YET\n+tRNl83Zg4u4MZvb9z/GvGgEVH2CtG3zukSGFRe7C7p344CnxezO6erQlcuX6L6NA77yktvKpwPL\nelvKcVlpJCN9uCJnkN8FFmof/bdJumnthe6bVC/4tUjKW+v5l3+hr31vehP6T954ia65/D2ur0tq\nEYnquSou7GLPMhipNoY2rJlegKG2cRaV5ecvmh4auvDyebHRyvAiFowB0qdRjLotFPPa8V+FOq95\nw5p+XbhkYcOyDC7tUfmjy7XzqUPae3S05ekmn1h/se643nK/fyKPwWAsIONB3BNFXxl5S//0i3/R\nidFT+sAlS7Tq4ncHXkBGCm9sd29vUc+8+E+RTSAeL53R/TtenLMQxdZNa9VT7Jp9TWmiPKchltTY\nehMmD1OGOeXI/QIypYmyTk1M6oEvvTQnhj5711V6z6Jzmh4vrMUBoqoTrcZ6FOVqdVGbqMoVhPP7\nv2Ht+4wol5Mp58uJBWT8OTVR1lf/wValIh0YHlOlUgl3Dn6DhVpqn7a1sjhebczc+bEVWn7B4qbz\nspstIJPEIi2S0XFFuXwwdgEZy7J+W9LnbNteb1nWxZK+IumspEOS7rFt2/jWaJz2v/pLvfLzk3p2\nz4gkabXVF/hYUTSQkpxAzCISgLtqbAxeMj9fdHZ4mw1g+uIAppXJmY/a29r00Ldfnv130qtu+mX6\n949sqUgaOjo6pyEWlkYLtQTd9sJNNWZGRk/pi988oMny2aaxX12YqdkxJeIQ8Yl0zqBlWf9J0k5J\n1Uc7n5f0Gdu2r9b0iMRbonz/tClNlPWjI28Yv8lvVBOIe4pd+uSNl8yO87/jhktmnwoylh5wVxsb\n+4+NacOaftcY8oLFAbxxy0c/OvJG6vMT3z/iEtVc4kb3Cs7fPfStA3MWvwpahi98fZ8m3p4KLfaJ\nQ8Qt6tr2qqTbJH115t+Dtm2/MPP/fy/peklPRlwGyNylip2uufw9uux9vylJvm5iAUwv4/6DAyf0\n2buuUmdHe6IxlJacAyB6bvkg6adg9ba9APIm0ieDtm1/W1JtF0ntOIBTkt4V5funTbG7oA8uP3dO\nz34YvWWmL1Xs1FPsmncTy4qEgDtnbHzqpsv0nkXnJNoQTFvO8cstH31w+bnkJ8BFnNuWNLpX8LLt\nRZjvB6RF5AvIWJZ1oaS/sW37g5Zljdi23T/z81skXWfb9uYmh8jdnMKxk6dVOv22igt+bXp8uY/X\nVpcDr/7d2MnTumvr9+eMy//S/dc1PW71b2t5+ZuoOT8fMCPSBWQiPHZogsaG17/z8zq3nCNpNldV\npT2O3fJt7b+RK5EvIBPx8SPhlg8+/2dXqyJ5usfx8z6St1iMIk5rj+n3+OQNhMjMBWQc9lmWdY1t\n289L+l1J/+jlj5JesSeJVYOKv9Yhladm39etDLUToT/ygX6d17tQf/13r0h6Z9K029j10qkzTbdF\nqD32hjX92r3/hO7++BVaecGilj9bq0xYxYkymFOGajmiZMJndKp37v2UtdFCC0FeJ0kqzN/P8JXX\nf6n9r47PLoz1kQ/064V9J3TnzZdlYrU853H9vI8pMeREufyJOgdJ6cpDVc57kM5Cu3YNHdd3Xnhd\nUjiLLbnlp2q5GpUtivP5zIv/1DBXOs+Xr9waIZPjinJ5FzQPxbXpfLVH63+V9FnLsl7UdEP0mzG9\nf6qUJspNJyA7J0I/u2dEh17/5bxJ00GGMLhtpvrRD71PD3/7QCoXRQDywkvuqH2tl0WZ/C7e1Lt4\nwZycc+fHVmj3wX+eszDWs3tGtOKintQutFLLzzkH8sZ5D/LxDUv1nRdeD22xJbf8dGqiPD1qKoS4\njCKnBn09EJXInwzatv3/Slo78//Dkq6N+j1N42cvqih6icKYpP3PY6f04SvOi3wcDIBg9r/6S/3o\nyBuSpA8uPzfUDZz9qs05bZL22qOJlSVMztxtSq9+q1jsB2GoV4+q+eDM5JSe+sE/zfu7M5NTrn8X\nRGehXUd+dlI7nzokqbW4zEp8A83E9WQwt5wTp/e/+su6E6n99BI5e9s2rOnXive/u+4TQGdDtBHn\nsdev7te7FnbpBwdOpHPSApBhpYmyxktndOz4Wxo6Oqqho6N65ecndapJrHsdNRB0gYTqogwLXRbG\n2rCmX4dfH0/VYgvOXJ6VXv2sL/aDeDSrR8XugnqKXVrWv2je9jcP7HzJU/1ze0rnzE/3fPwK7Xzq\nUMtxGSS+/eZKFp+BKah1IXJ7AlhNJpL08JMHNWD1zfn3ts3rAgd/bW9bd2eHFnYXtOriHrVpelxu\ndZio5L+Ha1n/Ig1YfapUpOeGjqtSqWjQmr+hNYDkOOf2tre3abJ8Vrv2juh3PtCvhU1yi9dRA81e\n1+zJ0qqL362L3/su/c4H+tXV2aE2Sf/ugxem5sbHLZdv3bQ24VK1zu1ztXJN8vqeEk8hs2S8dMZT\nPSpNlPXXf/eK2tvbtGpZny58T1GPff+YJt6eavh3UuN7mNr8lDS/I7GS3l4jTMR2evFkMCRh9K4G\n6SWq9rZVb/qK3QUdG3mr5R7siqSho6Pa88qbmiyflSRdu7qfIAcMMXby9Ly5vVcs7Z39fVfn/IVc\n3HhdVr3e67zmvoUzuar6tDDtuaS7s4NefZ94Cpk9z+4Z0Td2verrbybLZ7XnlTf18zdKs/cXjXi5\nh6nmp7CetrVyHL9bVWRhk3liO91oDIagXqIqdhd01y0rfO1FVe0l2rZ5XaDx6W5lqY7H98OZCO/8\n2AqtW3We7+MAiE97m2JtmLjlG+eWNFlQb3+yVvN12PwuZhPnMLWsDKvFO0oTZW1/bJ/2HxvTeg/7\nIzvr21XLz42k/g0u7dGX7r+u5bg0Lb5N4JZjiO30S3dXhMHaNN1T8pW/PaIBq0/XrDpPy8+f3pah\n2ZCAsC/G1R7sh5+cHmLhNeFmafgCkDXVVTtr43pZ/yLdcb1FvEagXj405VwHXeyCPI9WTZbP6vmh\n4xq0+vSJ9Rerp9hV97Vu9c3LPZHfe5jexQuabqHlBTHxDhbUyS6eDIbArXe1ImnHEwc18faUfnrk\nTf3FY/vmjKeOKsGE3YOdheELQFY54zru4Zdu+SbLGyebmg9b7ZmP43OxWEb2FLsLunfjgAodbapU\nKrry0iUNG4K1f+fsUGlWF3hKl6xGOYbYTj++rZA4e7uSfERueg82gPAkHdc8WYJX1JXs2bCmXxcu\nWSgp+u+UOmMuYjvdeDIYotreraR7SkztwQaQPeSbZCV9vfGDupI9fKfZ5yXHUA/Si28tQtWeEret\nHpLE8r9AupUmylIGF2pBY41yNz3zSKOw70eqx+tt8jr4R47JLr7NELkltWJ3IfRJt60kz2ZloaEI\nmK02hu+6ZYWutMK77SH+zeXlOpL090b9gR9h3xvVHu/ejQNaecGiOb/3Wz+pz/Ol8VzwPTbHMNGQ\n1NtjJewld5vt5dJoafFmZWGfGMBszhh+5DuHdOTnb4VybOLfXGlYup36Az/CrtPO4z34+L669zc/\nscd0qsl7UZ+zge/RGxqDIYjrQh1lYy4NNxsA5nth/4mWY5X4D8bvvn5ZRf2Bydw60b76D3bd+6S4\n6zN5JBrkJe9oDEbsteO/0gYPm7G2ykulT9MiAwDmK3YXdNctK2Zj+NrV/Tr4Wuu9nWcm5+/H5fYz\nvCPOHmdyN7Im7DrtPN7m2wcaHq9SkRGNA55cwQQ0BkNQL6mVJsp66Nsv67mh41q1rE+DVp+W9S9q\nfkCH0kRZYydPh5I86+3Vw80GkA5XWr26b+OABq0+/fDACX3qpstajtXuzo45nVbrV/eru7MjpBJn\nr+c7iR5nk/dZ4/qBIMKs06WJspb2L9LnZ463YU3/7O+c9fPa1f06MDxW91hx1WeeXEWLvOQdZyUk\njVZZmiyf1Z5X3lSho013XG/5Oq7bBOt6ewjefetKPfzk9GsbVfp6P2elKCAdlp+/SMvf/26VTp0J\nJVYXdhd06fmL9atTZyRJl56/WAtDygFhLxKRZybnZa4fCCKMuuIlx1Tr58joKT30rQOqVCoN75Oo\nz9nA9+gNTwZD5NxjpdVeiXq9RvX2cgmjl419YoB06F28INRYXXXxu3XH9ZbuuN7SqovfHcoxs9rz\nTY+zO64fiJufHFPsLmj5+Yv0ubs/5Ok+Ker6TB6JB3mpOc5OxMLulTgzOdXwOFR4AEGX0iZ/eEeP\nM2C+elt+mYI8AhPwZDAGQXslnL1G61f364GdLzHJGEBdJi1IkPWeb3qcgWQ1yjEm5cJGyCNIGrUv\nZGFvbjm4tEdbN63VN3a9queGjmuyfFYPP3lQ2zavI3kAmKN2yJQkI3IFPd9mYQNmZI1bjhk7eVqP\nPH1YA1afJOnL3z2spXd/iHoPuCAqQhTVQgldnR0aOjo6e4MHAGnCDZgZWMwHWeWWY9atOk/P7hmR\nJG1Y06+2uAsFpATDREMS5UIJxe6C7t04kNmhVgDCkfVhmQguq4v5APU8u2dktr7v2jsiutMBd9wl\npMSGNf26cMlCSfSyA6iPYZkAAMArngyGJI4eeSYZA/CCXAEnnhojT3oXL6C+Ax4RGSEaXNqjz29e\np4nJKXV1dnj+Oyb0A4hDnnPN2MnTs/u05hVPjZFG9fJWs3xGfQe8ITpCdmzkLV8T9JnQDyAOec41\nef7sTtwUI03qxa7XmKa+A80xTDREfifoM6EfQBzynGvy/NmBNKsXu8Q0EC4agy2qJqawXwsAfpFj\n4IZ6AYQrypgiXhE3GoMtGBoe15btu7Vl+24NDY83nKDvfK3EhH4A4XHLMVV5zjV5/uxS43oBmKxe\n7CYd01HGFPGKJLRVKsbvvFIZGyslWoDe3qKcZRgvndH9O16c3Qi+0NGmbZvXqdhdmDepebx0Rt/Y\n9ar2HxvTZPnsnNdK3hZ1cCtD3EwogynloAzmlGGmHFHuJ5x4DnJTe+5LE2Vt2b7bNR/VimMBGWe5\non4/zwodKp06Y0ZZakQZQ17rRdzl8sqt/phQLjcR5yApBXkoKkEWkAmjXG7HbyWmmpWr1WO3Is64\n8nNdMDjeTS1XoDxk1lUxJYaGx/XjV96s+/vaCl47yXn9mn49P3Rczga4aTcnALIp7t5zkxZt6V28\nQCpPJVoGeGda/UFy6uWtKPMZ9S8anFczMUzUp+rE5f3HxrR+Tf+8YQq1Y72dk5yf2zuigWW9uRum\nBCBaSQ+bckrTAg9Znp9jWr3wKk31B9nTqP5FGVN+j5223EVcm8v8q4KhJstn9fzQcQ1affrE+ovV\nU+ya1+OxrH/RvL+rvhYAwjS4tEdbN62VJHKMR3nopWavNeRRlPuKRhlTXo+dh9yF+PBk0KfanptK\npaIrL12inmKXa49HRZrTy7N+db+Ov3kq6Y8AIIOGhsd1/44Xdf+OFxNfeCANT6Ty1EtdXXQjLdJQ\nf2CuoeFx3bX1+4EXYfFS/6KMqWbHTmvuIq7NxbcQgJ9eoWX9izRg9alSkZ4bOq5de0dimxAMIB9q\nbw4k6eEnDyaeZ3gihVZQfxBEWLmQ+hcNzquZ+CYCcusluue2y/XSkTckSVctP3d2DuHQ0dE5q0MB\nQB4ksZqpV9Ve6oefnB5qRS+1efg+ECa/+cfU+pf23JWmsuYF30gA9RLK2UpFe4+OSpJWW32zr0lz\n0AIwXxryTKtzXKJoSNJLPc2kRjrQCmcu/NNbL1ebpJ/YY9r51CFJ2ZhjxxxxhInM71O9Gxrn0ISd\n3zmkX984oOXnL+KGA0DkTM4zrQ7dinKxBNPOVdxYiAJZM7i0R1+6/zqdOnVGx0be0lf/wZ4zQsuE\nYfStIm4RJhaQ8cHvpN0X9p+Y0+Oa5sQDwHxZzDNpXSwhDTi3yKrexQtUkbTjiYNybO2cesQtwkZj\nMCTF7oLuumXF7CpJ167u18HXkl3RDwBMwCpyAJJyYNh9X2gA02gM+tDshuZKq1f3bRzQoNWnHx44\noU/ddBkJBwD0zjDWbZvX+RrSREMyOpxbZFm1flcqFf3wwAndt3HAd/4xEXGLsFF7fGo2L2f5+YvU\n37dQkkVwAkCNoDnR5PmQace5RZZltX5n9XMhGdSgAJoFHoEJAOEir0aHc4ssy2r9zurnQvwYJgoA\nAAAAOURjEAAAAAByiMZgiEoTZZb3BWA8chUA+EPeRFYx4DgkbAAKIA3IVQDgD3kTWcaTwYBqe4jY\nABRAGpCr4sETBORRVus9eRNZx5PBAJw9REv7FyVcIgCACXiCgDyi3gPpxZNBn9x6iN4uT+muW1aw\nASgAo6V5s+I0PHXgCQLyKOv13rS8mYZciHRJx12A4R5/9lUdfHVc920cUH/fwtTcXAHInzRuVpzm\npw5nJqckpedcA5gv7LxZbcz5PVaacyHMxZNBn147/ittWNM/20O0YU2/9h8b08TbU/qLx/YlXTwA\naKrYXUhN4yRNTx2cTxDuuOESPbDzJW3ZvltDw+NJFw+IhGlPzqISVt4cGh7Xlu27feeFNOVCpEv2\nojVCpybK+tGRN9Te3qbVl/SpUpH+9cykrljaK0k6/DoXewDxCdq7jOhUnyCcmZzSAztf0sTb008G\nH37yoLZtXjfnuypNlKWTp5MqKhCaVp6cJZnH4n7vsZOnZxt0knteAOLGk0EfjvzspIaOjmrPkTdV\n/PUuHf3Z/6dl712sffao9tmjuv0jywhoALF4ds9IoN7ltEnjU4did0FdnR2aLJ+t+5rq04G7tn4/\n098f8iPIk7OgT8nCkOR7B5HGXIh0oDHoUWmirJ1PHZp9PP/c3hHd8/Er9Nd/98rszx595iiP7AFE\nrjRR1vbH9uVmuFD1qcO2zetSM0em0Y0bw72AZOMgqffuXbygpQZdGnMhzJdIl4JlWUOSfjXzz9dt\n2/5UEuVo1eJiV9JFAIBcSGMPeBoX6wEQrVbzArkEYYv9yaBlWd2SZNv2+pn/UtEQdOvl7Sl28cge\nQOyK3QXdu3GA3JMCbkPnGO4FJBsHScdgmhbxQvYlUROvkLTAsqxnZt7/M7Zt/ziBcvjm1ptDzy+A\nJGxY068LlyyURO5Jo+q1o7iwSypPJV0cIBFJ3kNx/wZMS2LO4L9K+r9s275B0iZJj1qWlZq5i/V6\neUkkAOJG7km3YndBvYsXJF0MIFFJ5jFyKCC1VSqVWN/Qsqxfk9Ru2/bEzL9/LOk227ZP1PmTeAvo\n0djMcuBcyAEjtEV4bCNzUCPkJyB2UeYgyeA8RL4BjBEoDyXRHfInki6XdI9lWb8l6Tck/aLRH4yN\nleIoV129vcU5ZRgaHteOJw5Kku6+dWUsKzo5y5AEE8pgSjkogzllqJYjSiZ8Rqd65z6J/OSlXEmj\nXP5QLn+izkGSmXno4M/e0vbH9klKJt/UY3I9oVzeUS5/guahJIZnflnSb1iW9YKkr0v6E9u262/G\nZBiWBAdgKvITgLjkbYsbIKtifzJo23ZZ0h/E/b4AAAAAgHekZuEWUyS9HDEA1EN+AhAXtrgBsoGo\nDYDliAGYivwEIC5scQOkH5EbEEkPgKnITwDiQr4B0o1hogAAAACQQzQGAQAAACCHaAwCAAAAQA7R\nGAQAAACAHKIxCAAAAAA5RGMQAAAAAHKIxiAAAAAA5BCNQQAAAADIIRqDAAAAAJBDNAYBAAAAIIdo\nDAIAAABADtEYBAAAAIAcojEIAAAAADlEY9CH0kRZpYly0sUAgFQhdwL5Rg4AzFVIugBpMTQ8rh1P\nHJQk3X3rSg0u7Um4RABgPnInkG/kAMBsPBn0YOzkae144qCmzlY0dbaih588SA8XADRRmiiTO4Ec\nIwcA/3979x5tR10dcPx7kxuIwA3QBdhKrZTXVmvQEHw0CARBtNEqWC1a1EWWD6BItYLycgkq4AOD\nCCJYREPxtcQSFVkSKKBgXIBilLTFLb6KFG2RqjxKgCS3f/zmmsPl3ORezMycZL6ftbJyz9y5Z/b5\nnfntM3t+v5kz+CwGJUmSJKmDLAYnYfttt+CoQ2YzPH2I4elDHHnwbEZmOsNWktZlZOawuVPqMHOA\nNPjskZO0527bseiYfQBMZJI0SeZOqdvMAdJgs1dOgUlMkqbO3Cl1mzlAGlxOE5UkSZKkDrIYlCRJ\nkqQOshiUJEmSpA6yGJQkSZKkDrIYlCRJkqQOshiUJEmSpA6yGJQkSZKkDrIYlCRJkqQOshiUJEmS\npA6yGJQkSZKkDrIYlCRJkqQOshiUJEmSpA6yGJQkSZKkDhpuO4BBd9/KVfCb/2s7DEmSanffylUA\nbN9yHNr0je1rIzM9FJXaZA9ch+/d/mvOX7ICgKMOmc2eu23XckSSJNWj9zPvHw6dw+ynbNNyRNpU\neXwlDQ6niU7gvpWrOH/JClavGWX1mlEu+PKK35/FkiRpUzL+M+/cLy73M0+18PhKGiwWg5IkSZLU\nQRaDExiZOcxRh8xmePoQw9OHOPLg2c5rlyRtksZ/5h3zt3P8zFMtPL6SBou9bx323G07Fh2zDyNb\nbQ6rVrcdjiRJtRn7zAPY+cnbcvfd97UckTZVvfuahaDULkcG12Nk5jDbb7tF22FIklS7kZnDHpyr\nEe5r0mCwGJQkSZKkDrIYlCRJkqQOshiUJEmSpA6yGJQkSZKkDrIYlCRJkqQOshiUJEmSpA6yGJQk\nSZKkDrIYlCRJkqQOshiUJEmSpA6yGJQkSZKkDrIYlCRJkqQOshiUJEmSpA4abnqDETEN+DiwB/AQ\n8MbM/EnTcUiSJElSl7UxMngwsFlmzgNOABa1EIMkSZIkdVobxeDewJUAmXkTsFcLMUiSJElSp7VR\nDM4C7u15vLqaOipJkiRJasjQ6OhooxuMiEXAjZl5afX4F5n55EaDkCRJkqSOa2NEbhmwACAingfc\n2kIMkiRJktRpjd9NFFgCvDAillWPF7YQgyRJkiR1WuPTRCVJkiRJ7fPGLZIkSZLUQRaDkiRJktRB\nFoOSJEmS1EEWg5IkSZLUQW3cTXS9ImIIuBP4UbXo25l5cvVVFGcDq4CrMvO9NccxDfg4sAfwEPDG\nzPxJndvs2fb3gN9VD38KvB9YDKwB/g04OjNruftPRDwX+EBm7h8Ru/bbbkS8CXgz5b04LTOvqDGG\nOcDlwO3Vrz+emZfWGUNEzAA+BTwF2Bw4DbiNBttighjuBL7G2r5Ra1tExHTgQmB3YBQ4ktIXFtPg\nPjFBHJtRc1tExCHAKzPzsOpxozlogphay0vriGm9OaPheCbdfxuOa9L9qcm4euLbAbgFOKCKp/W4\n2vwsXE9cJwJ/DcwAPkb52qwNGldEbA18Bhih5Lu3Z+aN5qHHxDKQ/b0nvkHsV7Xvv48jpmnAJyn5\ncQ3wJmB1W3ENwrHwJOJ6FnAOpZ0eAl6fmf8z1bgGdWRwF+CWzNy/+ndytfx84DWZ+XzguVUj1Olg\nYLPMnAecACyqeXsARMRMgJ7X/wbgLOCkzNwXGAJeXtO230k5WNm8WvSY7UbEHwPHAPOAFwHvj4jN\naoxhLnBWT3tcWncMwGHA3dXrfjFwHuX9b7It+sWwJ7CowbZ4KbCm6nPvAs6g+XboF8fp1NwWEfFR\nyusd6lncdA7qp5W8NJHJ5IwWwppU/20hrkn1pxbiGjug/gTwQBVH6+9jm5+F64lrPvCXVR+cD+xM\nPe/jPwJXZ+Z84HDKfgxwAeahXoPa3we1X82nmf13qg4Ctqz26/fSYn4chGPhScZ1NvCWzNwfuAw4\nPiKeONW4BrUYnAvsGBHXRsQVEbF7RMwCNs/Mn1XrLAUOrDmOvYErATLzJmCvmrc35pnAFhGxNCKu\nqc4C7pmZ11e//zr1vfYfA69g7QFwv+0+G1iWmY9k5r3V3+xRYwxzgZdExDcj4pMRsRXwnJpjuBR4\nd/XzNOARmm+LfjE02haZ+RXgiOrhTsBvgLlN7xN94vgt9bfFMuAoqv2wpRzUT1t5aSKTyRlNm2z/\nbdQU+lMbzqSc7Phl9bj19qLdz8J1OQhYERFfpsxa+Sr1vI8fAf6p+nkG8GBEjFCKMPPQWgPZ3yuD\n2K+a2n+n6kFg62p24NbAwy3GNQjHwpOJ69WZeWv18wxKG075OKj1YjAi3hARK3r/AXcBZ2TmCyhn\nBsamSdzb86f3UXaWOs0at83V1TB23R4AzszMF1GmEX123O/vp6bXnpmXUYaVx/SOioy1+SzWTtvp\nXV5XDDcBx2XmfpRpQqdQ9oc6Y3ggM++vPngvpZzF733va2+LPjGcDNxM822xOiIWAx+l7IuN7xMT\nxLFB2qJfDoqIuZn5xXGrjs8HTeSgftrKS32tJ2fUlqvWZRL9t5W4qtjW1Z9aiSsiDqeMrFxVLRoa\nhLho8bNwPbannIx6JSWuz/EHttcEx0K7ZubKagTiEuDE6nnNQz0Gtb8PcL/a4PvvBrIMmAn8kDKa\nek5bcQ3CsfBk4srMXwFExDzgaMoJpCnH1fo1g5l5EXBR77KIeALVi83MZRHxJMqLGelZbRZldKBO\n947b5rTMXFPzNqFcA/VjgMy8PSLuAeb0/H6E+l/7mN7XO9bm49tlhHKGuy5LMnNsx14CnAtcX3cM\nEfFkyrD7eZn5+Yj4UM+vG2mLcTF8ISK2bqMtMvPwaurBzZRkPabRfaInjpuAeZl5V/Wrx90W/XLQ\nBMa/xiZy0GTiaCovTVZvLE3mqkdZT/9tLS5YZ39qK66FwGhEHAg8C7iYcsDYdlyD9FnY69fAbZm5\nCvhRRKwEdvxD4pooD0XEbODzwLGZeUM1Q8E8NM6A9vdB7VcbfP/dQN5JGdE6OSL+FLiOMtrVVrEa\n2wAAB4ZJREFUdlwwGMfCfUXEocBJwILMvCciphxX6yODE3g38DaAiHgmcEc11PlwROxcDSEfRDnw\nq9MyYEEVx/OAW9e9+gazkGr+fVUIjwBXRcR+1e//ivpf+5jlfbZ7M7BPRGwe5QL3p1EuqK3LlRHx\n7OrnA4Hv1h1DdaB2FfDOzFxcLW60LSaIodG2iIjXRbnQHMr0g9XAd5veJ/rEsQa4rMm2aCkH9dNW\nXpqsfv2kUVPov03HNdn+1KjM3C8z51fXnXwfeD0l17QaF4P1WdjrW5Rr08bi2gK4ZkPHFRFPp4x0\nvSYzl4J5qJ9B7e8D3K8a2X8fhy1ZO9r8G8qAVevvY2UQjoUfIyJeSxkRnJ+ZP68WTzmu1kcGJ/AB\n4DMRsYAyQnh4tXxsmsh0YGlmfqfmOJYAL4yIZdXjhTVvb8xFwKcjYmynXwjcA1wY5SLQ/wC+VHMM\nY3drOnb8drPcQekc4AbKCYWTMvPhGmM4EjgvIh6hzLt/czUlpM4YTqIMq787IsauRXgrcE6DbdEv\nhrcBH2mwLb4ELI6Ib1LO0L2VMoWj6X2iXxx3UP9+Mcra/RCaz0H9tJWX1mfCnNFCLJPqvy3ENan+\n1EJc440yGO/jIHwWPkZmXhER+0bEzZQ88/fAz2uI6wzKXUTPiQiA32bmIZiHxhvU/j7eQPSrBvff\nqTqT0t9voOTHEyl3YW0zrkE4Fu4bV5Rp2R8F/pNychzgG5n5nqnGNTQ62sqddiVJkiRJLRrUaaKS\nJEmSpBpZDEqSJElSB1kMSpIkSVIHWQxKkiRJUgdZDEqSJElSB1kMSpIkSVIHDer3DKrjIuJjwN6U\n71jaDfh3YBawPfDUzLyrZ939gLMyc24bsUraOEXETsCPKPkFygnSWcDFmXnqJJ/jOGDL6rudlmfm\nnDpilTTYqnzyU+CgzPzXnuU/B/bNzDvaiUxaN0cGNZAy8y3VQdUC4L8yc05m7kL5ottXj1v99ZQv\nJ5akqRrLL3My85nAPOC4qL7BdxJ+/2W9FoJS5z1C+XLyrXqW+YXeGmiODGrQDY17/ClgEXAWQETM\nBF4CvL3huCRtmp5U/X9/RFwI/AXwRCCBV2Tmyog4FjgC+F/gV8D3ACJiTWZOi4gtgAuBPYA1wIcz\n85KGX4ek5t0FXEU5TjmiZ/lQRJwAvAqYDizNzOMj4nLgvMy8MiJOB+Zk5oKI+JPqeeYBX6DkIID3\nZOblEfENYEX1+5nA2zLz6oh4BnAOsBWwA7AoM8+NiFOBXSgzrbYDLsjMD0fEdOBMYL8qrsWZeXZE\nzAc+RBk0WpGZCzd8U2lQODKojc31wDYRsXv1+GDgmsz8XYsxSdp4PSkilkfEbRFxN/A+4BBgZ2Bl\nZs4DdgWeACyIiL2ANwFzgPmsLR57nQrcnZmzgRcAp0bE7NpfiaRBcBzwoog4sGfZi4E9gWdX/+8Y\nEYcBXwMOqNbZF3hqREyr1r+Ckot+lpl7Aa8Fnl+tOwoMV5fHHAZcHBEzgDcA78vM51Byz+k9MTwN\n2B+YCxwREXMouWy0ep7nAi+PiLFt7AbsbyG46bMY1EYlM0eBxcDfVYteh1NEJT1+d1XTO58OXEK5\nTvm6zLwBOD8ijqacad+NcrZ9P+BrmflAZq4EPtfnOfenykuZeQ/wFUrhKGkTl5n3UYqs3umiB1KK\nrVuqf3MpOecK4IBqvVHgB5Ri8cWUQvHbwMERsYRSCJ7Ws6kLqu19H/glMBs4FtiiGoU8HdiyWncU\nuCQzH6xOnn+VUiweALwsIpYDNwI7As+o1s/qtWgTZzGojdHFwKERsQOwe2Ze23ZAkjZu1Ymmd1Cm\nYx0XES8DPgvcT5mefj1l2vooj/7sXN3n6abx6Cnu0yhTsCR1QGZeDVxNdUkLpf+fPXZ9MmV65/sz\n805KfvgbYBnwTUrhOBdYlpk/Bp5KyUX7ADf3bKY390yrHl8KvJxyU6wTeXQe6l1/OrCq+v8dPXHt\nTTnhPgQ8+Ac0gTYiFoPa6GTmL4A7KNO5/rnlcCRtIjJzNWWK10mUa5G/mJkXA/9NmcI1HbiGciZ9\n64jYDHhln6e6ljJdi4jYjnJw9o3aX4CkQXIscBBlKvm1wOsiYsuIGAYuA15Rrfd14F3AddV6xwA3\nZuZoRBxFuU7wS8DRwA4RsXX1d4cBVFPXt6FcQ3ggcEpmXk41G6GadjoEvCoiZkTEtsBLgaXV9t4c\nEcMRMQLcADynrgbRYLIY1Mag3524Pg0spJzBkqTH61H5JTOXUqZL7QK8JiK+A3yCMtVzp8z8AfBh\nyhn6bwF39nmu9wJ/FBG3Us70n1ZN5ZK0aeu9u/DYdNFhyrTMfwFuohRtyzNz7GT2FcCfUfLJCmAG\nZYoowGeA6Mklp/TcI2HXiLiFMl300MxcQ7le+VsRsYwyongb8OdVXCspo4/fBs7IzB9Wf3s7sJyS\n0y7KzOur9b0LakcMjY76XkuSJEkbg4i4Djg+M29e78pl/VMoN8T6YL2RaWPkyKAkSZK0aXP0R305\nMihJkiRJHeTIoCRJkiR1kMWgJEmSJHWQxaAkSZIkdZDFoCRJkiR1kMWgJEmSJHXQ/wMaEt6vvMTA\nagAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x18c0dcf8>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# include a \"regression line\"\n",
      "sns.pairplot(data, x_vars=['TV','Radio','Newspaper'], y_vars='Sales', size=6, aspect=0.7, kind='reg')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "<seaborn.axisgrid.PairGrid at 0x18f516a0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Wv+T3+0cBvAfgMIA/kmX5Nb/f/1cAHgXwfRPbQGQJRtRaqqUuUCKVwdmr84VE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auzqxAFPm8vKkopVtdiSEoEp4/uwKFBDyiluDrjw0/fn9Gu++wjO3HycK+WI2gx6WC36IuOpRIL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\n1gOud871BhfPf41fLJ4LvMHMH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       "text": [
        "<matplotlib.figure.Figure at 0x18f51a90>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# scatter plot in Pandas\n",
      "fig, axs = plt.subplots(1, 3, sharey=True)\n",
      "data.plot(kind='scatter', x='TV', y='Sales', ax=axs[0], figsize=(16, 6))\n",
      "data.plot(kind='scatter', x='Radio', y='Sales', ax=axs[1])\n",
      "data.plot(kind='scatter', x='Newspaper', y='Sales', ax=axs[2])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x1a757358>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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1Jg2RHzjV93CbSO2V/S7/4GCWyGbJknGyBKn17EaqP69CRP6h1XEGgNaNm3EmWIRPjr6F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\n5ZRbLgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1a3bfe48>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Use a **scatter matrix** to visualize the relationship between all numerical variables."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# scatter matrix in Seaborn\n",
      "sns.pairplot(data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<seaborn.axisgrid.PairGrid at 0x40aae80>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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nDjz2rRlBEGMGNcEYUhVxSMkRWLyzVjM/siunhFLwga17G2UnzpUzJypGbNQL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\nIiI++P+xUUqVKByP0QAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1a528780>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# scatter matrix in Pandas\n",
      "pd.scatter_matrix(data, figsize=(12, 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000000001FE72470>,\n",
        "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000020114048>,\n",
        "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000000201E1668>,\n",
        "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000020206D68>],\n",
        "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000000203B22B0>,\n",
        "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000000204DB320>,\n",
        "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000000204F9470>,\n",
        "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000000206324E0>],\n",
        "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000000020505A90>,\n",
        "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000000207F5898>,\n",
        "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000002081D7B8>,\n",
        "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000002093E128>],\n",
        "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000000020964278>,\n",
        "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000020A74FD0>,\n",
        "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000020AA7D68>,\n",
        "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000020C14358>]], dtype=object)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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2f3nnOTJbr9fDUqezbWk4v3IVJS+pehIg8208WgNnIcRdAD4OYFUI8Q4A75dS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\nxMqU5yQsdVWnLlHOB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JffpJFnx1mSpA6t4/w6M7uO8Wod5/PMfDPcnUm6KybOkiQNj+mU\n9B8xcZYkSZI6mDhLkiRJHRycJUmSpA4OzpIkSVIHB2dJkiSpg4OzJEmS1MHBWZIkSerwCxYa7sFM\nVeEbAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1fe79fd0>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Use a **correlation matrix** to visualize the correlation between all numerical variables."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# compute correlation matrix\n",
      "data.corr()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>TV</th>\n",
        "      <th>Radio</th>\n",
        "      <th>Newspaper</th>\n",
        "      <th>Sales</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>TV</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.054809</td>\n",
        "      <td>0.056648</td>\n",
        "      <td>0.782224</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Radio</th>\n",
        "      <td>0.054809</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.354104</td>\n",
        "      <td>0.576223</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Newspaper</th>\n",
        "      <td>0.056648</td>\n",
        "      <td>0.354104</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.228299</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Sales</th>\n",
        "      <td>0.782224</td>\n",
        "      <td>0.576223</td>\n",
        "      <td>0.228299</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "                 TV     Radio  Newspaper     Sales\n",
        "TV         1.000000  0.054809   0.056648  0.782224\n",
        "Radio      0.054809  1.000000   0.354104  0.576223\n",
        "Newspaper  0.056648  0.354104   1.000000  0.228299\n",
        "Sales      0.782224  0.576223   0.228299  1.000000"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# display correlation matrix in Seaborn using a heatmap\n",
      "sns.heatmap(data.corr())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x20dbae80>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAVoAAAECCAYAAAC/jB/sAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1fe7b160>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Part 2: Simple linear regression"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Simple linear regression is an approach for predicting a **continuous response** using a **single feature**. It takes the following form:\n",
      "\n",
      "$y = \\beta_0 + \\beta_1x$\n",
      "\n",
      "- $y$ is the response\n",
      "- $x$ is the feature\n",
      "- $\\beta_0$ is the intercept\n",
      "- $\\beta_1$ is the coefficient for x\n",
      "\n",
      "$\\beta_0$ and $\\beta_1$ are called the **model coefficients**:\n",
      "\n",
      "- We must \"learn\" the values of these coefficients to create our model.\n",
      "- And once we've learned these coefficients, we can use the model to predict Sales."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Estimating (\"learning\") model coefficients\n",
      "\n",
      "- Coefficients are estimated during the model fitting process using the **least squares criterion**.\n",
      "- We are find the line (mathematically) which minimizes the **sum of squared residuals** (or \"sum of squared errors\")."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "![Estimating coefficients](images/estimating_coefficients.png)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In this diagram:\n",
      "\n",
      "- The black dots are the **observed values** of x and y.\n",
      "- The blue line is our **least squares line**.\n",
      "- The red lines are the **residuals**, which are the distances between the observed values and the least squares line."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "![Slope-intercept](images/slope_intercept.png)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "How do the model coefficients relate to the least squares line?\n",
      "\n",
      "- $\\beta_0$ is the **intercept** (the value of $y$ when $x$=0)\n",
      "- $\\beta_1$ is the **slope** (the change in $y$ divided by change in $x$)\n",
      "\n",
      "Let's estimate the model coefficients for the advertising data:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "### STATSMODELS ###\n",
      "\n",
      "# create a fitted model\n",
      "lm = smf.ols(formula='Sales ~ TV', data=data).fit()\n",
      "\n",
      "# print the coefficients\n",
      "lm.params"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "Intercept    7.032594\n",
        "TV           0.047537\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "### SCIKIT-LEARN ###\n",
      "\n",
      "# create X and y\n",
      "feature_cols = ['TV']\n",
      "X = data[feature_cols]\n",
      "y = data.Sales\n",
      "\n",
      "# instantiate and fit\n",
      "linreg = LinearRegression()\n",
      "linreg.fit(X, y)\n",
      "\n",
      "# print the coefficients\n",
      "print linreg.intercept_\n",
      "print linreg.coef_"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "7.03259354913\n",
        "[ 0.04753664]\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Interpreting model coefficients\n",
      "\n",
      "How do we interpret the TV coefficient ($\\beta_1$)?\n",
      "\n",
      "- A \"unit\" increase in TV ad spending is **associated with** a 0.0475 \"unit\" increase in Sales.\n",
      "- Meaning: An additional $1,000 spent on TV ads is **associated with** an increase in sales of 47.5 widgets.\n",
      "- This is not a statement of **causation**.\n",
      "\n",
      "If an increase in TV ad spending was associated with a **decrease** in sales, $\\beta_1$ would be **negative**."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Using the model for prediction\n",
      "\n",
      "Let's say that there was a new market where the TV advertising spend was **$50,000**. What would we predict for the Sales in that market?\n",
      "\n",
      "$$y = \\beta_0 + \\beta_1x$$\n",
      "$$y = 7.0326 + 0.0475 \\times 50$$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# manually calculate the prediction\n",
      "7.0326 + 0.0475*50"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "9.4076"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "### STATSMODELS ###\n",
      "\n",
      "# you have to create a DataFrame since the Statsmodels formula interface expects it\n",
      "X_new = pd.DataFrame({'TV': [50]})\n",
      "\n",
      "# predict for a new observation\n",
      "lm.predict(X_new)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "array([ 9.40942557])"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "### SCIKIT-LEARN ###\n",
      "\n",
      "# predict for a new observation\n",
      "linreg.predict(50)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "array([ 9.40942557])"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Thus, we would predict Sales of **9,409 widgets** in that market."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Does the scale of the features matter?\n",
      "\n",
      "Let's say that TV was measured in dollars, rather than thousands of dollars. How would that affect the model?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['TV_dollars'] = data.TV * 1000\n",
      "data.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>TV</th>\n",
        "      <th>Radio</th>\n",
        "      <th>Newspaper</th>\n",
        "      <th>Sales</th>\n",
        "      <th>TV_dollars</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>230.1</td>\n",
        "      <td>37.8</td>\n",
        "      <td>69.2</td>\n",
        "      <td>22.1</td>\n",
        "      <td>230100</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>44.5</td>\n",
        "      <td>39.3</td>\n",
        "      <td>45.1</td>\n",
        "      <td>10.4</td>\n",
        "      <td>44500</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>17.2</td>\n",
        "      <td>45.9</td>\n",
        "      <td>69.3</td>\n",
        "      <td>9.3</td>\n",
        "      <td>17200</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>151.5</td>\n",
        "      <td>41.3</td>\n",
        "      <td>58.5</td>\n",
        "      <td>18.5</td>\n",
        "      <td>151500</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>180.8</td>\n",
        "      <td>10.8</td>\n",
        "      <td>58.4</td>\n",
        "      <td>12.9</td>\n",
        "      <td>180800</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "      TV  Radio  Newspaper  Sales  TV_dollars\n",
        "1  230.1   37.8       69.2   22.1      230100\n",
        "2   44.5   39.3       45.1   10.4       44500\n",
        "3   17.2   45.9       69.3    9.3       17200\n",
        "4  151.5   41.3       58.5   18.5      151500\n",
        "5  180.8   10.8       58.4   12.9      180800"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "### SCIKIT-LEARN ###\n",
      "\n",
      "# create X and y\n",
      "feature_cols = ['TV_dollars']\n",
      "X = data[feature_cols]\n",
      "y = data.Sales\n",
      "\n",
      "# instantiate and fit\n",
      "linreg = LinearRegression()\n",
      "linreg.fit(X, y)\n",
      "\n",
      "# print the coefficients\n",
      "print linreg.intercept_\n",
      "print linreg.coef_"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "7.03259354913\n",
        "[  4.75366404e-05]\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "How do we interpret the TV_dollars coefficient ($\\beta_1$)?\n",
      "\n",
      "- A \"unit\" increase in TV ad spending is **associated with** a 0.0000475 \"unit\" increase in Sales.\n",
      "- Meaning: An additional dollar spent on TV ads is **associated with** an increase in sales of 0.0475 widgets.\n",
      "- Meaning: An additional $1,000 spent on TV ads is **associated with** an increase in sales of 47.5 widgets."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# predict for a new observation\n",
      "linreg.predict(50000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "array([ 9.40942557])"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The scale of the features is **irrelevant** for linear regression models, since it will only affect the scale of the coefficients, and we simply change our interpretation of the coefficients."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Part 3: A deeper understanding"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Bias and variance\n",
      "\n",
      "Linear regression is a low variance/high bias model:\n",
      "\n",
      "- **Low variance:** Under repeated sampling from the underlying population, the line will stay roughly in the same place\n",
      "- **High bias:** The line will rarely fit the data well\n",
      "\n",
      "A closely related concept is **confidence intervals**."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Confidence intervals\n",
      "\n",
      "Statsmodels calculates 95% confidence intervals for our model coefficients, which are interpreted as follows: If the population from which this sample was drawn was **sampled 100 times**, approximately **95 of those confidence intervals** would contain the \"true\" coefficient."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "### STATSMODELS ###\n",
      "\n",
      "# print the confidence intervals for the model coefficients\n",
      "lm.conf_int()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Intercept</th>\n",
        "      <td>6.129719</td>\n",
        "      <td>7.935468</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TV</th>\n",
        "      <td>0.042231</td>\n",
        "      <td>0.052843</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "                  0         1\n",
        "Intercept  6.129719  7.935468\n",
        "TV         0.042231  0.052843"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- We only have a **single sample of data**, and not the **entire population of data**.\n",
      "- The \"true\" coefficient is either within this interval or it isn't, but there's no way to actually know.\n",
      "- We estimate the coefficient with the data we do have, and we show uncertainty about that estimate by giving a range that the coefficient is **probably** within.\n",
      "- From Quora: [What is a confidence interval in layman's terms?](http://www.quora.com/What-is-a-confidence-interval-in-laymans-terms/answer/Michael-Hochster)\n",
      "\n",
      "Note: 95% confidence intervals are just a convention. You can create 90% confidence intervals (which will be more narrow), 99% confidence intervals (which will be wider), or whatever intervals you like.\n",
      "\n",
      "A closely related concept is **hypothesis testing**."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Hypothesis testing and p-values\n",
      "\n",
      "General process for hypothesis testing:\n",
      "\n",
      "- You start with a **null hypothesis** and an **alternative hypothesis** (that is opposite the null).\n",
      "- You check whether the data supports **rejecting the null hypothesis** or **failing to reject the null hypothesis**.\n",
      "\n",
      "For model coefficients, here is the conventional hypothesis test:\n",
      "\n",
      "- **null hypothesis:** There is no relationship between TV ads and Sales (and thus $\\beta_1$ equals zero)\n",
      "- **alternative hypothesis:** There is a relationship between TV ads and Sales (and thus $\\beta_1$ is not equal to zero)\n",
      "\n",
      "How do we test this hypothesis?\n",
      "\n",
      "- The **p-value** is the probability that the relationship we are observing is occurring purely by chance.\n",
      "- If the 95% confidence interval for a coefficient **does not include zero**, the p-value will be **less than 0.05**, and we will reject the null (and thus believe the alternative).\n",
      "- If the 95% confidence interval **includes zero**, the p-value will be **greater than 0.05**, and we will fail to reject the null."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "### STATSMODELS ###\n",
      "\n",
      "# print the p-values for the model coefficients\n",
      "lm.pvalues"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "Intercept    1.406300e-35\n",
        "TV           1.467390e-42\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Thus, a p-value less than 0.05 is one way to decide whether there is **likely** a relationship between the feature and the response. In this case, the p-value for TV is far less than 0.05, and so we **believe** that there is a relationship between TV ads and Sales.\n",
      "\n",
      "Note that we generally ignore the p-value for the intercept."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## How well does the model fit the data?\n",
      "\n",
      "R-squared:\n",
      "\n",
      "- A common way to evaluate the overall fit of a linear model\n",
      "- Defined as the **proportion of variance explained**, meaning the proportion of variance in the observed data that is explained by the model\n",
      "- Also defined as the reduction in error over the **null model**, which is the model that simply predicts the mean of the observed response\n",
      "- Between 0 and 1, and higher is better\n",
      "\n",
      "Here's an example of what R-squared \"looks like\":"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "![R-squared](images/r_squared.png)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's calculate the R-squared value for our simple linear model:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "### STATSMODELS ###\n",
      "\n",
      "# print the R-squared value for the model\n",
      "lm.rsquared"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "0.61187505085007099"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "### SCIKIT-LEARN ###\n",
      "\n",
      "# calculate the R-squared value for the model\n",
      "y_pred = linreg.predict(X)\n",
      "metrics.r2_score(y, y_pred)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "0.61187505085007099"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- The threshold for a **\"good\" R-squared value** is highly dependent on the particular domain.\n",
      "- R-squared is more useful as a tool for **comparing models**."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Part 4: Multiple Linear Regression\n",
      "\n",
      "Simple linear regression can easily be extended to include multiple features, which is called **multiple linear regression**:\n",
      "\n",
      "$y = \\beta_0 + \\beta_1x_1 + ... + \\beta_nx_n$\n",
      "\n",
      "Each $x$ represents a different feature, and each feature has its own coefficient:\n",
      "\n",
      "$y = \\beta_0 + \\beta_1 \\times TV + \\beta_2 \\times Radio + \\beta_3 \\times Newspaper$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "### SCIKIT-LEARN ###\n",
      "\n",
      "# create X and y\n",
      "feature_cols = ['TV', 'Radio', 'Newspaper']\n",
      "X = data[feature_cols]\n",
      "y = data.Sales\n",
      "\n",
      "# instantiate and fit\n",
      "linreg = LinearRegression()\n",
      "linreg.fit(X, y)\n",
      "\n",
      "# print the coefficients\n",
      "print linreg.intercept_\n",
      "print linreg.coef_"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.93888936946\n",
        "[ 0.04576465  0.18853002 -0.00103749]\n"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# pair the feature names with the coefficients\n",
      "zip(feature_cols, linreg.coef_)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "[('TV', 0.04576464545539765),\n",
        " ('Radio', 0.18853001691820462),\n",
        " ('Newspaper', -0.0010374930424762799)]"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For a given amount of Radio and Newspaper spending, an increase of $1000 in **TV** spending is associated with an **increase in Sales of 45.8 widgets**.\n",
      "\n",
      "For a given amount of TV and Newspaper spending, an increase of $1000 in **Radio** spending is associated with an **increase in Sales of 188.5 widgets**.\n",
      "\n",
      "For a given amount of TV and Radio spending, an increase of $1000 in **Newspaper** spending is associated with an **decrease in Sales of 1.0 widgets**. How could that be?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Feature selection\n",
      "\n",
      "How do I decide **which features to include** in a linear model?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Using p-values\n",
      "\n",
      "We could try a model with all features, and only keep features in the model if they have **small p-values**:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "### STATSMODELS ###\n",
      "\n",
      "# create a fitted model with all three features\n",
      "lm = smf.ols(formula='Sales ~ TV + Radio + Newspaper', data=data).fit()\n",
      "\n",
      "# print the p-values for the model coefficients\n",
      "print lm.pvalues"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Intercept    1.267295e-17\n",
        "TV           1.509960e-81\n",
        "Radio        1.505339e-54\n",
        "Newspaper    8.599151e-01\n",
        "dtype: float64\n"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This indicates we would reject the null hypothesis for **TV and Radio** (that there is no association between those features and Sales), and fail to reject the null hypothesis for **Newspaper**. Thus, we would keep TV and Radio in the model.\n",
      "\n",
      "However, this approach has **drawbacks**:\n",
      "\n",
      "- Linear models rely upon a lot of **assumptions** (such as the features being independent), and if those assumptions are violated (which they usually are), p-values are less reliable.\n",
      "- Using a p-value cutoff of 0.05 means that if you add 100 features to a model that are **pure noise**, 5 of them (on average) will still be counted as significant."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Using R-squared\n",
      "\n",
      "We could try models with different sets of features, and **compare their R-squared values**:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# R-squared value for the model with two features\n",
      "lm = smf.ols(formula='Sales ~ TV + Radio', data=data).fit()\n",
      "lm.rsquared"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "0.89719426108289557"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# R-squared value for the model with three features\n",
      "lm = smf.ols(formula='Sales ~ TV + Radio + Newspaper', data=data).fit()\n",
      "lm.rsquared"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "0.89721063817895219"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This would seem to indicate that the best model includes **all three features**. Is that right?\n",
      "\n",
      "- R-squared will always increase as you add more features to the model, even if they are **unrelated** to the response.\n",
      "- As such, using R-squared as a model evaluation metric can lead to **overfitting**.\n",
      "- **Adjusted R-squared** is an alternative that penalizes model complexity (to control for overfitting), but it generally [under-penalizes complexity](http://scott.fortmann-roe.com/docs/MeasuringError.html).\n",
      "\n",
      "As well, R-squared depends on the same assumptions as p-values, and it's less reliable if those assumptions are violated."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Using train/test split (or cross-validation)\n",
      "\n",
      "A better approach to feature selection!\n",
      "\n",
      "- They attempt to directly estimate how well your model will **generalize** to out-of-sample data.\n",
      "- They rely on **fewer assumptions** that linear regression.\n",
      "- They can easily be applied to **any model**, not just linear models."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Evaluation metrics for regression problems\n",
      "\n",
      "Evaluation metrics for classification problems, such as **accuracy**, are not useful for regression problems. We need evaluation metrics designed for comparing **continuous values**.\n",
      "\n",
      "Let's create some example numeric predictions, and calculate three common evaluation metrics for regression problems:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# define true and predicted response values\n",
      "y_true = [100, 50, 30, 20]\n",
      "y_pred = [90, 50, 50, 30]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Mean Absolute Error** (MAE) is the mean of the absolute value of the errors:\n",
      "\n",
      "$$\\frac 1n\\sum_{i=1}^n|y_i-\\hat{y}_i|$$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print metrics.mean_absolute_error(y_true, y_pred)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10.0\n"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Mean Squared Error** (MSE) is the mean of the squared errors:\n",
      "\n",
      "$$\\frac 1n\\sum_{i=1}^n(y_i-\\hat{y}_i)^2$$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print metrics.mean_squared_error(y_true, y_pred)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "150.0\n"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Root Mean Squared Error** (RMSE) is the square root of the mean of the squared errors:\n",
      "\n",
      "$$\\sqrt{\\frac 1n\\sum_{i=1}^n(y_i-\\hat{y}_i)^2}$$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print np.sqrt(metrics.mean_squared_error(y_true, y_pred))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "12.2474487139\n"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Comparing these metrics:\n",
      "\n",
      "- **MAE** is the easiest to understand, because it's the average error.\n",
      "- **MSE** is more popular than MAE, because MSE \"punishes\" larger errors, which tends to be useful in the real world.\n",
      "- **RMSE** is even more popular than MSE, because RMSE is interpretable in the \"y\" units.\n",
      "\n",
      "All of these are **loss functions**, because we want to minimize them.\n",
      "\n",
      "Here's an additional example, to demonstrate how MSE/RMSE punish larger errors:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# same true values as above\n",
      "y_true = [100, 50, 30, 20]\n",
      "\n",
      "# new set of predicted values\n",
      "y_pred = [60, 50, 30, 20]\n",
      "\n",
      "# MAE is the same as before\n",
      "print metrics.mean_absolute_error(y_true, y_pred)\n",
      "\n",
      "# RMSE is larger than before\n",
      "print np.sqrt(metrics.mean_squared_error(y_true, y_pred))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10.0\n",
        "20.0\n"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Using train/test split for feature selection\n",
      "\n",
      "Let's use train/test split with RMSE to decide whether Newspaper should be kept in the model:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# define a function that accepts X and y and computes testing RMSE\n",
      "def train_test_rmse(X, y):\n",
      "    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)\n",
      "    linreg = LinearRegression()\n",
      "    linreg.fit(X_train, y_train)\n",
      "    y_pred = linreg.predict(X_test)\n",
      "    return np.sqrt(metrics.mean_squared_error(y_test, y_pred))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# include Newspaper\n",
      "feature_cols = ['TV', 'Radio', 'Newspaper']\n",
      "X = data[feature_cols]\n",
      "train_test_rmse(X, y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "1.4046514230328948"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# exclude Newspaper\n",
      "feature_cols = ['TV', 'Radio']\n",
      "X = data[feature_cols]\n",
      "train_test_rmse(X, y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "1.3879034699382886"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Comparing linear regression with other models\n",
      "\n",
      "Advantages of linear regression:\n",
      "\n",
      "- Simple to explain\n",
      "- Highly interpretable\n",
      "- Model training and prediction are fast\n",
      "- No tuning is required (excluding regularization)\n",
      "- Features don't need scaling\n",
      "- Can perform well with a small number of observations\n",
      "\n",
      "Disadvantages of linear regression:\n",
      "\n",
      "- Presumes a linear relationship between the features and the response\n",
      "- Performance is (generally) not competitive with the best supervised learning methods due to high bias\n",
      "- Sensitive to irrelevant features\n",
      "- Can't automatically learn feature interactions"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}